Jetson Nano圖片識別範例

臺灣黑熊辨識....

https://devblogs.nvidia.com/jetson-nano-ai-computing/
此頁面下的Hello AI world提供許多範例，下例先以圖片識別(Coding your own image recognition)做Demo；

先下載jetson-inference整個專案並編譯，
參閱https://github.com/dusty-nv/jetson-inference/blob/master/docs/building-repo.md

接著把jetson-inference/examples/下的my-recognition資料夾整個複製回家目錄下
~$ cp -R ~/jetson-inference/examples/my-recognition/ ~/

進入剛複製的my-recognition下會有6個檔，其中三個為示範用圖片

接著在my-recognition下build example
~$ cmake .

~$ make

到此大致上已經完成，接著先來識別範例內的北極熊是否能判別出來:

~$ ./my-recognition polar_bear.jpg

顯示結果為
class 0296 - 0.999999  (ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus)

image is recognized as 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus' (class #296) with 99.999878% confidence

我們再拿一張臺灣黑熊的照片來試試

tseng@jetsonnano:~/my-recognition$ ./my-recognition  bb1.jpg

loaded image  bb1.jpg  (184 x 274)  806656 bytes

[cuda]  cudaAllocMapped 806656 bytes, CPU 0x100c30000 GPU 0x100c30000

imageNet -- loading classification network model from:

         -- prototxt     networks/googlenet.prototxt

         -- model        networks/bvlc_googlenet.caffemodel

         -- class_labels networks/ilsvrc12_synset_words.txt

         -- input_blob   'data'

         -- output_blob  'prob'

         -- batch_size   2

[TRT]  TensorRT version 5.0.6

[TRT]  detected model format - caffe  (extension '.caffemodel')

[TRT]  desired precision specified for GPU: FASTEST

[TRT]  requested fasted precision for device GPU without providing valid calibrator, disabling INT8

[TRT]  native precisions detected for GPU:  FP32, FP16

[TRT]  selecting fastest native precision for GPU:  FP16

[TRT]  attempting to open engine cache file /usr/local/bin/networks/bvlc_googlenet.caffemodel.2.1.GPU.FP16.engine

[TRT]  loading network profile from engine cache... /usr/local/bin/networks/bvlc_googlenet.caffemodel.2.1.GPU.FP16.engine

[TRT]  device GPU, /usr/local/bin/networks/bvlc_googlenet.caffemodel loaded

[TRT]  device GPU, CUDA engine context initialized with 2 bindings

[TRT]  binding -- index   0

               -- name    'data'

               -- type    FP32

               -- in/out  INPUT

               -- # dims  3

               -- dim #0  3 (CHANNEL)

               -- dim #1  224 (SPATIAL)

               -- dim #2  224 (SPATIAL)

[TRT]  binding -- index   1

               -- name    'prob'

               -- type    FP32

               -- in/out  OUTPUT

               -- # dims  3

               -- dim #0  1000 (CHANNEL)

               -- dim #1  1 (SPATIAL)

               -- dim #2  1 (SPATIAL)

[TRT]  binding to input 0 data  binding index:  0

[TRT]  binding to input 0 data  dims (b=2 c=3 h=224 w=224) size=1204224

[cuda]  cudaAllocMapped 1204224 bytes, CPU 0x100f30000 GPU 0x100f30000

[TRT]  binding to output 0 prob  binding index:  1

[TRT]  binding to output 0 prob  dims (b=2 c=1000 h=1 w=1) size=8000

[cuda]  cudaAllocMapped 8000 bytes, CPU 0x101060000 GPU 0x101060000

device GPU, /usr/local/bin/networks/bvlc_googlenet.caffemodel initialized.

[TRT]  networks/bvlc_googlenet.caffemodel loaded

imageNet -- loaded 1000 class info entries

networks/bvlc_googlenet.caffemodel initialized.

class 0295 - 0.730006  (American black bear, black bear, Ursus americanus, Euarctos americanus)

class 0297 - 0.231508  (sloth bear, Melursus ursinus, Ursus ursinus)

class 0367 - 0.011617  (chimpanzee, chimp, Pan troglodytes)

image is recognized as 'American black bear, black bear, Ursus americanus, Euarctos americanus' (class #295) with 73.000565% confidence

結果辨識為黑熊...

換石虎試試...

tseng@jetsonnano:~/my-recognition$ ./my-recognition  st1.jpg

loaded image  st1.jpg  (273 x 184)  803712 bytes

[cuda]  cudaAllocMapped 803712 bytes, CPU 0x100c30000 GPU 0x100c30000

imageNet -- loading classification network model from:

         -- prototxt     networks/googlenet.prototxt

         -- model        networks/bvlc_googlenet.caffemodel

         -- class_labels networks/ilsvrc12_synset_words.txt

         -- input_blob   'data'

         -- output_blob  'prob'

         -- batch_size   2

[TRT]  TensorRT version 5.0.6

[TRT]  detected model format - caffe  (extension '.caffemodel')

[TRT]  desired precision specified for GPU: FASTEST

[TRT]  requested fasted precision for device GPU without providing valid calibrator, disabling INT8

[TRT]  native precisions detected for GPU:  FP32, FP16

[TRT]  selecting fastest native precision for GPU:  FP16

[TRT]  attempting to open engine cache file /usr/local/bin/networks/bvlc_googlenet.caffemodel.2.1.GPU.FP16.engine

[TRT]  loading network profile from engine cache... /usr/local/bin/networks/bvlc_googlenet.caffemodel.2.1.GPU.FP16.engine

[TRT]  device GPU, /usr/local/bin/networks/bvlc_googlenet.caffemodel loaded

[TRT]  device GPU, CUDA engine context initialized with 2 bindings

[TRT]  binding -- index   0

               -- name    'data'

               -- type    FP32

               -- in/out  INPUT

               -- # dims  3

               -- dim #0  3 (CHANNEL)

               -- dim #1  224 (SPATIAL)

               -- dim #2  224 (SPATIAL)

[TRT]  binding -- index   1

               -- name    'prob'

               -- type    FP32

               -- in/out  OUTPUT

               -- # dims  3

               -- dim #0  1000 (CHANNEL)

               -- dim #1  1 (SPATIAL)

               -- dim #2  1 (SPATIAL)

[TRT]  binding to input 0 data  binding index:  0

[TRT]  binding to input 0 data  dims (b=2 c=3 h=224 w=224) size=1204224

[cuda]  cudaAllocMapped 1204224 bytes, CPU 0x100f30000 GPU 0x100f30000

[TRT]  binding to output 0 prob  binding index:  1

[TRT]  binding to output 0 prob  dims (b=2 c=1000 h=1 w=1) size=8000

[cuda]  cudaAllocMapped 8000 bytes, CPU 0x101060000 GPU 0x101060000

device GPU, /usr/local/bin/networks/bvlc_googlenet.caffemodel initialized.

[TRT]  networks/bvlc_googlenet.caffemodel loaded

imageNet -- loaded 1000 class info entries

networks/bvlc_googlenet.caffemodel initialized.

class 0038 - 0.010387  (banded gecko)

class 0285 - 0.100883  (Egyptian cat)

class 0287 - 0.838111  (lynx, catamount)

class 0288 - 0.023775  (leopard, Panthera pardus)

image is recognized as 'lynx, catamount' (class #287) with 83.811073% confidence

辨識為"山貓"...